papers-we-love_papers-we-love/time_series

Important papers relating to time-series data

Time-series data presents specific but very common problems for efficient analysis, resulting in the need for columnar data stores and iterative one-pass processing.

The included documents are:

• 📜 Operators on Inhomogeneous Time Series - Gilles O. Zumbach and Ulrich A. Müller

  We present a toolbox to compute and extract information from inhomogeneous (i.e. unequally spaced) time series. The toolbox contains a large set of operators, mapping from the space of inhomogeneous time series to itself.

  These operators are computationally efficient (time and memory-wise) and suitable for stochastic processes. This makes them attractive for processing high-frequency data in finance and other fields. Using a basic set of operators, we easily construct more powerful combined operators which cover a wide set of typical applications.

• 📜 ASAP: Automatic Smoothing for Attention Prioritization in Streaming Time Series Visualization - Kexin Rong, Peter Bailis

  Time Series smoothing method to better prioritize attention in time series exploration and monitoring visualizations, smooth the time series as much as possible to remove noise while still retaining large-scale structure. We develop a new technique for automatically smoothing streaming time series that adaptively optimizes this trade-off between noise reduction (i.e., variance) and outlier retention (i.e., kurtosis).